AI agents are good at repetitive tasks that have huge amount of documentation and data. These tasks usually have a converged solution pattern and can be handled by an AI agent.
Development loop
flowchart TB
clarify-requirements --> design-system
design-system --> implement-system
implement-system --> verify-requirement-fulfillment
verify-requirement-fulfillment -- failure --> debug-implementation
debug-implementation --> verify-requirement-fulfillment
verify-requirement-fulfillment -- success --> write-tests
write-tests --> verify-tests
verify-tests -- failure --> debug-tests
debug-tests --> verify-tests
verify-tests -- success --> complete
Claude
Agent setting
- Use
CLAUDE.mdfor essential context, e.g., configuring Claude to ask followup questions if a request is unclear.
Chat
- Use
@<FILE_NAME>to include the content of a file in the context. - Use
/compactto compress conversation history. - Use
/accountto view token quota. - Use
/modelto switch the backend LLM. - Use
/pluginsto find and install plugins.
Skills
- Use skills for context injection and let Claude decide to load or not.
- Pick and use only well-tested skills from the public repository as many skills are ineffective and even malicious.
- Use Anthropic skill-creator skill to build our own skills.
Hooks
- Use hooks for event-driven context injection.
Subagent
- Use
/agentsto create new agents.
MCP
- Use Model Context Protocol (MCP) to let Claude make API calls to web services.